# Bare bones particle swarms

@article{Kennedy2003BareBP, title={Bare bones particle swarms}, author={James Kennedy}, journal={Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706)}, year={2003}, pages={80-87} }

The particle swarm algorithm has just enough moving parts to make it hard to understand. The formula is very simple, it is even easy to describe the working of the algorithm verbally, yet it is very difficult to grasp in one's mind how the particles oscillate around centers that are constantly changing; how they influence one another; how the various parameters affect the trajectory of the particle; how the topology of the swarm affects its performance; and so on. This paper strips away some…

## 645 Citations

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A new hierarchical bare bones particle swarm optimization algorithm is proposed in this work and a mutated structure for the local group is presented in this paper.

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A BBPSO with dynamic local search (DLS-BBPSO) is proposed to solve the problems of premature convergence and rapid diversity-losing of particles in the bare bones particle swarm optimization.

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The results indicate that the modified bare bones particle swarm optimization can be an efficient alternative due to the smaller confidence intervals and fast convergence characteristics.

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The present paper decomposes and reinterprets the particle swarm in order to discover new ways of implementing the algorithm, and shows that the traditional trajectory formulas can be replaced with random number generators sampling from various symmetrical probability distributions.

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A distribution-guided BPSO (DBPSO) in which an adaptive jump operation is introduced to help the particle get out of the local optimal and each dimension of the particle is assigned a jump probability according to the evolutionary state of the swarm.

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A pair-wise bare bones particle swarm optimization algorithm is proposed in this paper to balance the exploration and exploitation of PBBPSO with nonlinear functions.

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It is proven that the proposed velocity update rule can effectively address all of these issues at the same time and comparisons with earlier methods show that the overall ability of the proposed method for solving benchmark functions is significantly better.

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A novel particle swarm optimization (PSO) called self-adaptive escape PSO, which is guaranteed to converge to the global optimization solution with probability one is proposed, which can not only significantly speed up the convergence, but also effectively solve the premature convergence problem.

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Experimental results on test functions up to 1000 dimensions show that BBPSO-DE is a very competitive algorithm for solving both unimodal and multimodal problems.

## References

SHOWING 1-10 OF 13 REFERENCES

The swarm and the queen: towards a deterministic and adaptive particle swarm optimization

- Computer ScienceProceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
- 1999

A very simple particle swarm optimization iterative algorithm is presented, with just one equation and one social/confidence parameter, and the results are good enough so that it is certainly worthwhile trying the method on more complex problems.

The particle swarm - explosion, stability, and convergence in a multidimensional complex space

- Computer ScienceIEEE Trans. Evol. Comput.
- 2002

This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.

Particle swarm optimization: surfing the waves

- Computer ScienceProceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
- 1999

This paper takes the next step, generalizing to obtain closed form equations for trajectories of particles in a multi-dimensional search space.

The Behavior of Particles

- BusinessEvolutionary Programming
- 1998

A simplified version of the particle swarm algorithm is examined in an effort to understand the trajectories of particles as they search for solutions and address optimal parameter values.

Flocks, herds, and schools: a distributed behavioral model

- Computer Science
- 1998

This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually, an elaboration of a particle system, with the simulated birds being the particles.

Population structure and particle swarm performance

- Computer ScienceProceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
- 2002

The effects of various population topologies on the particle swarm algorithm were systematically investigated and it was discovered that previous assumptions may not have been correct.

Particle swarm optimization

- Computer ScienceProceedings of ICNN'95 - International Conference on Neural Networks
- 1995

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is…

Experimental study of particle swarm optimization

- 2000

A stochastic nonlinear model

- 1990